The Division of Science is pleased once again to announce the availability of Traineeships for Undergraduates in Computational Neuroscience through a grant from the National Institute on Drug Abuse.Traineeships will commence in summer 2019 and run through the academic year 2019-20.

From former trainee Dahlia Kushinksy’s first-author paper published this month in Journal of Experimental Biology, “In vivo effects of temperature on the heart and pyloric rhythms in the crab, Cancer borealis”

Please apply to the program by February 27, 2019 at 6 pm to be considered.

Traineeships in Computational Neuroscience are intended to provide intensive undergraduate training in computational neuroscience for students interested in eventually pursuing graduate research. The traineeships will provide approximately $5000 in stipend to support research in the summer, and $3000 each for fall and spring semesters during the academic year. Current Brandeis sophomores and juniors (classes of ’20, ’21) may apply. To be eligible to compete for this program, you must

have a GPA > 3.0 in Div. of Science courses

have a commitment from a professor to advise you on a research project related to computational neuroscience

have a course work plan to complete requirements for a major in the Division of Science

Joshua Trachtenberg, graduated from Brandeis in 1990 and is a leader in studying the living brain in action using advanced imaging technology. After establishing his research laboratory at UCLA, he founded a company – Neurolabware – in order to build the sophisticated custom research microscopes that are needed to perform groundbreaking work in understanding how the brain develops and how diseases and injuries interrupt its normal functioning. His company is created by scientists and for scientists, and is unique in creating high quality microscopes that are easy to use but also have the flexibility to be used in creative ways in innovative experiments, and in combination with a variety of other devices.

Brandeis University is now seeking to acquire one of these advanced microscopes that can observe fundamental processes inside the living brains of animals engaged in advanced behaviors. The reasonant scanning two-photon microscope from Neurolabware allows researchers to understand and image large networks of neurons in order to visualize which cells and networks are involved with specific memories or how these processes go awry in disease. “This approach is unparalleled. There is no other technique around that could possibly touch this,” Trachtenberg says.

Previous two-photon technologies permitted only very slow imaging, allowing scientists to take a picture about every two seconds, but the resonant two-photon technology is a major breakthrough that allows scientists to take pictures at about 30 frames per second. This speed increase is a major game changer. Not only can one observe activity in the brain at a higher speed, but it is possible to take pictures at a speed that is faster than the movement artifacts that must be accounted for, such as breathing or heart beats. Because one can see the movement, it can be corrected, allowing high resolution functional imaging of structures as small as single synaptic spines in the living brain. Further, advances in laser technology and fluorescent labels now allow scientists to see deeper into the brain than ever before, compounding the recent advantages of increased speed.

A new feature was added to the 2018 Life Sciences Holiday Party – the Ugly Sweater Contest! Lab’s were encouraged to purchase, design, and bedazzle a sweater for their PIs to wear and show off at the party. Ballots for best sweater were cast at the event with the Marder lab submitting the winner. Eve’s sweater was decked out with crabs, lobsters, STG’s and neurons. Congratulations!

Computational Neuroscience is an exciting branch of science, which is helping us understand how simple biophysical processes within cells such as neurons lead to complex and sometimes surprising neural responses, and how these neurons, when connected in circuits can give rise to the wide range of activity patterns underlying human thinking and behavior. To bridge the scales from molecules to mental activity, computer simulations of mathematical models are essential, as it is all too easy for us otherwise to produce descriptions of these complex interacting systems that are internally inconsistent. Simulations allow us to ask “given these ingredients, what is possible?”

Simulation showing how weaker input that is localized can produce spiking when stronger dispersed input does not.

The best way to study computational neuroscience is to write the computer codes that model a particular biological phenomenon, then see what the simulation does when you vary a parameter in the model. Therefore, the course I teach at Brandeis (NBIO 136B) is based around a large number of computer tutorials, in which students, some of whom have no computer-coding background, begin with codes of 5-10 lines that simulate charging of a capacitor, and end up completing codes that simulate the neural underpinnings of learning, pattern recognition, memory, and decision-making. It turns out that very few computational principles are needed to build such codes, making these simulation methods far more easily understood and completed than any mathematical analysis of the systems. However, in the absence of a suitable introductory textbook—most computational neuroscience textbooks are designed by Ph.D. physicists and mathematicians for Ph.D. physicists and mathematicians—it proved difficult for me to use the flipped classroom approach (see below). Therefore, my goal was to create a text that students could read and understand on their own.

Different behaviors of a three-unit circuit as connection-strengths are changed. (Multistable constant activity states, multiple oscillating states, chaotic activity, heteroclinic state sequence). Each color represents firing rate of a unit as a function of time.

In keeping with the goal of the course—to help students gain coding expertise and understand biological systems through manipulations of computer codes—I produced over 100 computer codes (in Matlab) for the book, the vast majority of which are freely available online. (All codes used to produce figures and some tutorial solutions are accessible, but I retained over half of the tutorial solutions in case instructors wish to assign tutorials without students being able to seek a solution elsewhere.)

I designed this book to help beginning students access the exciting and blossoming field of computational neuroscience and lead them to the point where they can understand, simulate, and analyze the quite complex behaviors of individual neurons and brain circuits. I was motivated to write the book when progressing to the “flipped” or “inverted” classroom approach to teaching, in which much of the time in the classroom is spent assisting students with the computer tutorials while the majority of information-delivery is via students reading the material outside of class. To facilitate this process, I assume less mathematical background of the reader than is required for many similar texts (I confine calculus-based proofs to appendices) and intersperse the text with computer tutorials that can be used in (or outside of) class. Many of the topics are discussed in more depth in the book “Theoretical Neuroscience” by Peter Dayan and Larry Abbott, the book I used to learn theoretical neuroscience and which I recommend for students with a strong mathematical background.

The majority of figures, as well as the tutorials, have associated computer codes available online, at github.com/primon23/Intro-Comp-Neuro, and at my website. I hope these codes may be a useful resource for anyone teaching or wishing to further their understanding of neural systems.

Scifest VIII, our annual Poster Session featuring undergraduate researchers, will be held on Thursday, August 2. The poster session will be 1:00 to 3:00 pm in the Shapiro Science Center atrium.

SciFest features undergrads who have spent their summers working in both on-campus and off-campus labs doing scientific research, usually alongside grad students, postdocs and faculty members. It an opportunity for these dedicated students from across the Division of Science, including summer visitors and Brandeis students, to present their research for peers and the community.

As of today, 107 students have registered to present.

The public is invited to attend and to discuss research with the students. As always, refreshments will be served.

Students often tell me that they don’t want to be scientists because it is too lonely. That always surprises me, because laboratories are filled with people. One of the conclusions that readers of Charlotte Nassim’s “Lessons from the Lobster” should take from the book is that laboratories are communities of scholars of all ages. Lifelong friendships are often formed and sustained as laboratory colleagues may spend as much time together as they do with other friends and family. When Charlotte approached me about writing the story of my research, I was very surprised because there are many eminent neuroscientists, including many other eminent female neuroscientists. What convinced me to work with Charlotte was her wish to reach teenage girls, before they decided that a career in science was not for them. And this decision was validated when a few days ago, one of the students (now working in a neighboring lab) whom I had taught in NBio 140, Principles of Neuroscience, told me that she loved the book, but wished she had had it when she was in high school. We agreed that after she finished the book, that she would donate it to her small home town library, in the hopes that it would encourage other high school students to consider becoming scientists.

Charlotte’s book is a piece of science history. She read our lab notebooks, and talked to many ex-lab members. Her choices of what to emphasize and how to frame the scientific issues speak as much about what she finds scientifically and sociologically interesting as it does about what I was thinking. By reading deeply, she relied not only on my flawed memory, but on what I and others had written. For me, it is an extraordinary reminder that even scientists who revere data have only partial recollections of their own intellectual paths.